Methods and systems for mapping cardiac activity

ABSTRACT

Cardiac activity can be mapped by receiving an electrogram, transforming the electrogram into the wavelet domain (e.g., using a continuous wavelet transformation) to create a scalogram of the electrogram, computing at least one energy function of the scalogram, and computing at least one metric of the electrogram using the at least one energy function. The metrics of the electrogram can include, without limitation: a QRS activity duration for the electrogram; a near-field component duration for the electrogram; a far-field component duration for the electrogram; a number of multiple components for the electrogram; a slope of a sharpest component of the electrogram; a scalogram width; an energy ratio in the electrogram; and a cycle-length based metric of the electrogram.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. provisional application No.62/301,866, filed 1 Mar. 2016, which is hereby incorporated by referenceas though fully set forth herein.

BACKGROUND

The instant disclosure relates to electrophysiological mapping, such asmay be performed in cardiac diagnostic and therapeutic procedures. Inparticular, the instant disclosure relates to systems, apparatuses, andmethods for mapping multi-component cardiac activity.

While mapping within and around scar or wall thinning, such as insubjects with Ischemic or Dilated Cardiomyopathy, sharp fractionatedbi-polar potentials representing the local near-field activity canappear to be fused with the far-field electrogram. These sharppotentials often take one of two morphology forms. In Form 1, thenear-field potentials are separated from the far-field potentials by anisoelectric line and extend beyond the QRS end of a surface ECG. In Form2, the near-field potentials appear fused with the far-field potentialand buried within the QRS activity of the surface ECG.

It would be desirable to be able to detect such multi-component signalsand to decouple the various components thereof.

BRIEF SUMMARY

Disclosed herein is a method of mapping cardiac activity, including:receiving an electrogram signal S(t) at a signal processor; and usingthe signal processor: transforming the electrogram signal S(t) into thewavelet domain, thereby computing a scalogram. G(f, t); computing atleast one energy function. L(t) of the scalogram G(f, t); and computingat least one metric of the electrogram signal S(t) using the at leastone energy function. L(t). The electrogram signal S(t) can betransformed into the wavelet domain by applying a continuous wavelettransformation to the electrogram signal S(t) to compute the scalogramG(f, t). Further, values of G(f, t) less than a preset noise thresholdcan be set to zero. The method can also include generating a graphicalrepresentation of the at least one metric of the electrogram signal S(t)on a cardiac model.

In embodiments, the at least one energy function L(t) is of formL(t)=ΣG(f,t), where f can be between 0 Hz and 1000 Hz. According toother aspects of the disclosure, f can be within a cardiac activityfrequency range defined by a preset lower frequency limit and a presetupper frequency limit.

It is contemplated that the step of computing at least one metric of theelectrograin signal S(t) using the at least one energy function L(t) caninclude computing a QRS activity duration for the electrogram signalS(t), such as by: computing a pulse wave L^(Pulse)(t) having a pulseduration according to an equation

${L^{Pulse}(t)} = \left\{ {\begin{matrix}{1,{{{if}\mspace{14mu}{L(t)}} > 0}} \\{0,{otherwise}}\end{matrix};} \right.$and defining the QRS activity duration for the electrogram signal S(t)to be equal to the pulse duration.

The method can also include: detecting a plurality of local maximumpeaks in the at least one energy function L(t); and categorizing eachlocal maximum peak of the plurality of local maximum peaks as anear-field peak, a far-field peak, or a noise peak. For example, a localmaximum peak can be categorized a near-field peak if the at least oneenergy function L(t) exceeds a preset near field threshold at the localmaximum peak; as a far-field peak if the at least one energy functionL(t) exceeds a preset far field threshold and not the preset near fieldthreshold at the local maximum peak; and as a noise peak otherwise.

In other embodiments disclosed herein, the step of computing at leastone metric of the electrogram signal S(t) using the at least one energyfunction L(t) includes computing at least one of a near-field componentduration and a far-field component duration for the electrogram signalS(t) by a method including the following steps: computing a pulse waveL^(Pulse)(t) having one or more pulses according to an equation

${L^{Pulse}(t)} = \left\{ {\begin{matrix}{1,{{{if}\mspace{14mu}{L(t)}} > 0}} \\{0,{otherwise}}\end{matrix};} \right.$and for each pulse of the one or more pulses of the pulse waveL^(Pulse)(t): if the pulse includes a near-field peak, defining thenear-field component duration for the electrogram signal S(t) to beequal to a duration of the pulse; and if the pulse includes a far-fieldpeak, defining the far-field component duration for the electrogramsignal S(t) to be equal to the duration of the pulse.

In still further embodiments, the step of computing at least one metricof the electrogram signal S(t) using the at least one energy functionL(t) can include computing a number of multiple components for theelectrogram signal S(t), wherein the number of multiple components forthe electrogram signal S(t) is defined to be equal to a total number ofthe plurality of local maximum peaks in the at least one energy functionL(t).

In yet further embodiments of the disclosure, the step of computing atleast one metric of the electrogram signal S(t) using the at least oneenergy function L(t) can include computing a slope of a sharpestcomponent of the electrogram S(t). This can be accomplished, forexample, by: identifying a maximum energy near-field peak of theplurality of local maximum peaks in the at least one energy functionL(t); computing a maximum value of a first derivative S′(t) of theelectrogram signal S(t) within a preset refractory window surroundingthe maximum energy near-field peak; and defining the maximum value ofthe first derivative S′(t) of the electrogram signal S(t) within thepreset refractory window as the slope of the sharpest component of theelectrogram signal S(t). Alternatively, a scalogram width can be used todetermine the slope of the sharpest component.

In still further embodiments disclosed herein, the step of computing atleast one energy function L(t) of form L(t)=ΣG(f,t) includes: computinga high frequency energy function L^(High)(t)=ΣG(f^(High),t); andcomputing a low frequency energy function L^(Low)(t)=ΣG(f^(Low),t); andcomputing at least one metric of the electrogram signal S(t) using theat least one energy function L(t) includes computing a ratio ofL^(High)(t) to L^(Low)(t). f^(High) can be between 60 Hz and 300 Hz andf^(Low) can be between 10 Hz and 60 Hz.

It is also contemplated that the step of computing at least one metricof the electrogram signal S(t) using the at least one energy functionL(t) can include computing a cycle-length based metric using the ratioof L^(High)(t) to L^(Low)(t). For example, computing the cycle-lengthbased metric using the ratio of L^(High)(t) to L^(Low)(t) can includecomputing the cycle-length based metric based upon a plurality of localactivation times detected when the ratio of L^(High)(t) to L^(Low)(t)exceeds a preset threshold.

Also disclosed herein is a system for mapping cardiac activity,including: a wavelet transformation processor configured: to receive anelectrogram S(t); to transform the electrogram signal S(t) into thewavelet domain, thereby computing a scalogram G(f,t); and to compute atleast one energy function L(t) of the scalogram G(f,t); and a mappingprocessor configured to compute at least one metric of the electrogramsignal S(t) in the wavelet domain using the at least one energy functionL(t). The mapping processor can be further configured to output agraphical representation of the at least one metric of the electrogramsignal S(t) on a cardiac model. The at least one metric of theelectrogram signal S(t) can be selected from the group consisting of: aQRS activity duration for the electrogram signal S(t); a near-fieldcomponent duration for the electrogram signal S(t); a far-fieldcomponent duration for the electrogram signal S(t); a number of multiplecomponents for the electrogram signal S(t); a slope of a sharpestcomponent of the electrogram signal S(t); a scalogram width; an energyratio in the electrogram signal S(t); and a cycle-length based metric ofthe electrogram signal S(t).

The foregoing and other aspects, features, details, utilities, andadvantages of the present invention will be apparent from reading thefollowing description and claims, and from reviewing the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic of an electrophysiology system, such as may beused in an electrophysiology study including mapping cardiacrepolarization activity.

FIG. 2 depicts an exemplary multi-electrode catheter used in anelectrophysiology study.

FIG. 3 is a flowchart of representative steps that can be followed tomap cardiac activity.

FIG. 4 depicts an electrogram from healthy tissue and the correspondingscalogram.

FIG. 5A depicts an energy function of the scalogram of FIG. 4.

FIG. 5B depicts a bounded energy function of the scalogram of FIG. 4.

FIG. 6A depicts an electrogram having morphology Form 1 and thecorresponding scalogram.

FIG. 6B depicts a bounded energy function of the scalogram of FIG. 6A.

FIG. 7A depicts an electrogram having morphology Form 2 and thecorresponding scalogram.

FIG. 7B depicts a bounded energy function of the scalogram of FIG. 7A.

FIG. 8 is a flowchart of representative steps that can be followed toidentify and categorize peaks in an energy function.

DETAILED DESCRIPTION

The present disclosure provides methods, apparatuses, and systems forthe creation of electrophysiology maps (e.g., electrocardiographic maps)that provide information regarding cardiac activity. Certain embodimentsof the disclosure will be explained with reference to the use of bipolarelectrograms to create electrophysiology maps of wavelet domain metrics.It should be understood, however, that the teachings herein can beapplied in other contexts where it is desirable to discern a localizedphenomenon from a far-field phenomenon. For example, the teachingsherein can be applied to differentiate fetal cardiac signals frommaternal cardiac signals or to differentiate localized brain activityfrom more distant brain activity in an electroencephalogram (“EEG”).

FIG. 1 shows a schematic diagram of an electrophysiology system 8 forconducting cardiac electrophysiology studies by navigating a cardiaccatheter and measuring electrical activity occurring in a heart 10 of apatient 11 and three-dimensionally mapping the electrical activityand/or information related to or representative of the electricalactivity so measured. System 8 can be used, for example, to create ananatomical model of the patient's heart 10 using one or more electrodes.System 8 can also be used to measure electrophysiology data, including,but not limited to, electrical activation data (e.g., local activationtime (“LAT”)), at a plurality of points along a cardiac surface andstore the measured data in association with location information foreach measurement point at which the electrophysiology data was measured,for example to create an electrophysiology map of the patient's heart 10(or a portion thereof).

As one of ordinary skill in the art will recognize, and as will befurther described below, system 8 can determine the location, and insome aspects the orientation, of objects, typically within athree-dimensional space, and express those locations as positioninformation determined relative to at least one reference.

For simplicity of illustration, the patient 11 is depicted schematicallyas an oval. In the embodiment shown in FIG. 1, three sets of surfaceelectrodes (e.g., patch electrodes) are shown applied to a surface ofthe patient 11, defining three generally orthogonal axes, referred toherein as an x-axis, a y-axis, and a z-axis. In other embodiments theelectrodes could be positioned in other arrangements, for examplemultiple electrodes on a particular body surface. As a furtheralternative, the electrodes do not need to be on the body surface, butcould be positioned internally to the body or on an external frame.

In FIG. 1, the x-axis surface electrodes 12, 14 are applied to thepatient along a first axis, such as on the lateral sides of the thoraxregion of the patient (e.g., applied to the patient's skin underneatheach arm) and may be referred to as the Left and Right electrodes. They-axis electrodes 18, 19 are applied to the patient along a second axisgenerally orthogonal to the x-axis, such as along the inner thigh andneck regions of the patient, and may be referred to as the Left Leg andNeck electrodes. The z-axis electrodes 16, 22 are applied along a thirdaxis generally orthogonal to both the x-axis and the y-axis, such asalong the sternum and spine of the patient in the thorax region, and maybe referred to as the Chest and Back electrodes. The heart 10 liesbetween these pairs of surface electrodes 12/14, 18/19, and 16/22.

An additional surface reference electrode (e.g., a “belly patch”) 21provides a reference and/or ground electrode for the system 8. The bellypatch electrode 21 may be an alternative to a fixed intra-cardiacelectrode 31, described in further detail below. It should also beappreciated that, in addition, the patient 11 may have most or all ofthe conventional electrocardiogram (“ECG” or “EKG”) system leads inplace. In certain embodiments, for example, a standard set of 12 ECGleads may be utilized for sensing electrocardiograms on the patient'sheart 10. This ECG information is available to the system 8 (e.g., itcan be provided as input to computer system 20). Insofar as ECG leadsare well understood, and for the sake of clarity in the figures, onlyone lead 6 and its connection to computer system 20 is illustrated inFIG. 1.

A representative catheter 13 having at least one electrode 17 (e.g., adistal electrode) is also depicted in schematic fashion in FIG. 1. Thisrepresentative catheter electrode 17 can be referred to as a“measurement electrode” or a “roving electrode.” Typically, multipleelectrodes on catheter 13, or on multiple such catheters, will be used.In one embodiment, for example, system 8 may utilize sixty-fourelectrodes on twelve catheters disposed within the heart and/orvasculature of the patient.

In other embodiments, system 8 may utilize a single catheter thatincludes multiple (e.g., eight) splines, each of which in turn includesmultiple (e.g., eight) electrodes. Of course, these embodiments aremerely exemplary, and any number of electrodes and catheters may beused. Indeed, in some embodiments, a high density mapping catheter, suchas the EnSite™ Array™ non-contact mapping catheter of St. Jude Medical,Inc., can be utilized.

Likewise, it should be understood that catheter 13 (or multiple suchcatheters) are typically introduced into the heart and/or vasculature ofthe patient via one or more introducers and using familiar procedures.For purposes of this disclosure, a segment of an exemplarymulti-electrode catheter 13 is shown in FIG. 2. In FIG. 2, catheter 13extends into the left ventricle 50 of the patient's heart 10 through atransseptal sheath 35. The use of a transseptal approach to the leftventricle is well known and will be familiar to those of ordinary skillin the art, and need not be further described herein. Of course,catheter 13 can also be introduced into the heart 10 in any othersuitable manner.

Catheter 13 includes electrode 17 on its distal tip, as well as aplurality of additional measurement electrodes 52, 54, 56 spaced alongits length in the illustrated embodiment. Typically, the spacing betweenadjacent electrodes will be known, though it should be understood thatthe electrodes may not be evenly spaced along catheter 13 or of equalsize to each other. Since each of these electrodes 17, 52, 54, 56 lieswithin the patient, location data may be collected simultaneously foreach of the electrodes by system 8.

Similarly, each of electrodes 17, 52, 54, and 56 can be used to gatherelectrophysiological data from the cardiac surface. The ordinarilyskilled artisan will be familiar with various modalities for theacquisition and processing of electrophysiology data points (including,for example, both contact and non-contact electrophysiological mappingand the collection of both unipolar and bipolar electrograms), such thatfurther discussion thereof is not necessary to the understanding of thecardiac repolarization activity mapping techniques disclosed herein.Likewise, various techniques familiar in the art can be used to generatea graphical representation from the plurality of electrophysiology datapoints. In so far as the ordinarily skilled artisan will appreciate howto create electrophysiology maps from electrophysiology data points, theaspects thereof will only be described herein to the extent necessary tounderstand the maps disclosed herein.

Returning now to FIG. 1, in some embodiments, a fixed referenceelectrode 31 (e.g., attached to a wall of the heart 10) is shown on asecond catheter 29. For calibration purposes, this electrode 31 may bestationary (e.g., attached to or near the wall of the heart) or disposedin a fixed spatial relationship with the roving electrodes (e.g.,electrodes 17, 52, 54, 56), and thus may be referred to as a“navigational reference” or “local reference.” The fixed referenceelectrode 31 may be used in addition or alternatively to the surfacereference electrode 21 described above. In many instances, a coronarysinus electrode or other fixed electrode in the heart 10 can be used asa reference for measuring voltages and displacements; that is, asdescribed below, fixed reference electrode 31 may define the origin of acoordinate system.

Each surface electrode is coupled to a multiplex switch 24, and thepairs of surface electrodes are selected by software running on acomputer 20, which couples the surface electrodes to a signal generator25. Alternately, switch 24 may be eliminated and multiple (e.g., three)instances of signal generator 25 may be provided, one for eachmeasurement axis (that is, each surface electrode pairing).

The computer 20, for example, may comprise a conventionalgeneral-purpose computer, a special-purpose computer, a distributedcomputer, or any other type of computer. The computer 20 may compriseone or more processors 28, such as a single central processing unit(CPU), or a plurality of processing units, commonly referred to as aparallel processing environment, which may execute instructions topractice the various aspects disclosed herein.

Generally, three nominally orthogonal electric fields are generated by aseries of driven and sensed electric dipoles (e.g., surface electrodepairs 12/14, 18/19, and 16/22) in order to realize catheter navigationin a biological conductor. Alternatively, these orthogonal fields can bedecomposed and any pairs of surface electrodes can be driven as dipolesto provide effective electrode triangulation. Likewise, the electrodes12, 14, 18, 19, 16, and 22 (or any other number of electrodes) could bepositioned in any other effective arrangement for driving a current toor sensing a current from an electrode in the heart. For example,multiple electrodes could be placed on the back, sides, and/or belly ofpatient 11. For any desired axis, the potentials measured across theroving electrodes resulting from a predetermined set of drive(source-sink) configurations may be combined algebraically to yield thesame effective potential as would be obtained by simply driving auniform current along the orthogonal axes.

Thus, any two of the surface electrodes 12, 14, 16, 18, 19, 22 may beselected as a dipole source and drain with respect to a groundreference, such as belly patch 21, while the unexcited electrodesmeasure voltage with respect to the ground reference. The rovingelectrodes 17, 52, 54, 56 placed in the heart 10 are exposed to thefield from a current pulse and are measured with respect to ground, suchas belly patch 21. In practice the catheters within the heart 10 maycontain more or fewer electrodes than the four shown, and each electrodepotential may be measured. As previously noted, at least one electrodemay be fixed to the interior surface of the heart to form a fixedreference electrode 31, which is also measured with respect to ground,such as belly patch 21, and which may be defined as the origin of thecoordinate system relative to which localization system 8 measurespositions. Data sets from each of the surface electrodes, the internalelectrodes, and the virtual electrodes may all be used to determine thelocation of the roving electrodes 17, 52, 54, 56 within heart 10.

The measured voltages may be used by system 8 to determine the locationin three-dimensional space of the electrodes inside the heart, such asroving electrodes 17, 52, 54, 56, relative to a reference location, suchas reference electrode 31. That is, the voltages measured at referenceelectrode 31 may be used to define the origin of a coordinate system,while the voltages measured at roving electrodes 17, 52, 54, 56 may beused to express the location of roving electrodes 17, 52, 54, 56relative to the origin. In some embodiments, the coordinate system is athree-dimensional (x, y, z) Cartesian coordinate system, although othercoordinate systems, such as polar, spherical, and cylindrical coordinatesystems, are contemplated.

As should be clear from the foregoing discussion, the data used todetermine the location of the electrode(s) within the heart is measuredwhile the surface electrode pairs impress an electric field on theheart. The electrode data may also be used to create a respirationcompensation value used to improve the raw location data for theelectrode locations as described in U.S. Pat. No. 7,263,397, which ishereby incorporated herein by reference in its entirety. The electrodedata may also be used to compensate for changes in the impedance of thebody of the patient as described, for example, in U.S. Pat. No.7,885,707, which is also incorporated herein by reference in itsentirety.

In one representative embodiment, the system 8 first selects a set ofsurface electrodes and then drives them with current pulses. While thecurrent pulses are being delivered, electrical activity, such as thevoltages measured with at least one of the remaining surface electrodesand in vivo electrodes, is measured and stored. Compensation forartifacts, such as respiration and/or impedance shifting, may beperformed as indicated above.

In some embodiments, system 8 is the EnSite™ Velocity™ cardiac mappingand visualization system of St. Jude Medical, Inc., which generateselectrical fields as described above, or another localization systemthat relies upon electrical fields. Other localization systems, however,may be used in connection with the present teachings, including forexample, systems that utilize magnetic fields instead of or in additionto electrical fields for localization. Examples of such systems include,without limitation, the CARTO navigation and location system of BiosenseWebster, Inc., the AURORA® system of Northern Digital Inc., Sterotaxis'NIOBE® Magnetic Navigation. System, as well as MediGuide™ Technology andthe EnSite Precision™ system, both from St. Jude Medical, Inc.

The localization and mapping systems described in the following patents(all of which are hereby incorporated by reference in their entireties)can also be used with the present invention: U.S. Pat. Nos. 6,990,370;6,978,168; 6,947,785; 6,939,309; 6,728,562; 6,640,119; 5,983,126; and5,697,377.

One basic methodology of mapping cardiac activity will be explained withreference to the flowchart 300 of representative steps presented as FIG.3. In some embodiments, for example, flowchart 300 may represent severalexemplary steps that can be carried out by the computer 20 of FIG. 1(e.g., by one or more processors 28 executing one or more specializedmodules, such as a wavelet transformation processor executing a wavelettransformation module as further described below) to generate a map ofcardiac activity as described herein. It should be understood that therepresentative steps described below can be hardware-implemented,software-implemented, or both. For the sake of explanation, the term“signal processor” is used herein to describe both hardware- andsoftware-based implementations of the teachings herein. Likewise, itshould be understood that the teachings herein can be executed on asingle CPU, which may have one or more threads, or distributed acrossmultiple CPUs, each of which may have one or more threads, in a parallelprocessing environment.

In step 302, an electrogram signal, denoted S(t) (and illustrated astrace 402 in panel A of FIG. 4), is received at a signal processor(e.g., by one or more processors 28 within computer 20). According toaspects of the disclosure, the electrogram signal S(t) is a bipolarsignal. It is contemplated, however, that the teachings herein can alsobe applied to unipolar electrogram signals and/or to monophasic actionpotential (“MAP”) signals.

In block 304, the electrogram signal S(t) is transformed into thewavelet domain, which computes a scalogram G(f,t) (illustrated asscalogram 404 in panel B of FIG. 4). More specifically, G(f,t) can becomputed for a preset window, referred to herein as a “Roving ActivationInterval” (“RAI”) about a reference time point, referred to herein asT_(ref). According to aspects disclosed herein, T_(ref) corresponds toQRS activity detected using a user-defined reference cardiac signal,such as the signal from an EKG lead or the signal from an in vivoreference electrode.

Likewise, the width of the RAI can be user-defined. According to aspectsof the disclosure, the RAI is between about 100 ms and about 300 mswide.

For purposes of illustration, the figures herein are displayed in thefull width of an exemplary RAI centered about an exemplary T_(ref).

In embodiments, block 304 applies a continuous wavelet transform to theelectrogram signal S(t). The mother wavelet used in the wavelettransform can be a high time-resolution mother wavelet, such as a Paulwavelet, or a high frequency-resolution mother wavelet, such as a Morletwavelet, both of which will be familiar to those of ordinary skill inthe art. Of course, other mother wavelets can also be employed withoutdeparting from the scope of the instant teachings. The teachings hereincan also be applied using discrete wavelet transforms.

If desired, noise can be removed from the scalogam G(f,t) in block 306.For example, the energy amplitudes within the RAI can be normalized tovalues between 0 and 1, with the highest energy amplitude within the RAIcorresponding to 1. Once the energy amplitudes have been so normalized,values of G(f,t) less than a preset, and optionally user-defined, noisethreshold, such as about 0.2, can be set to zero, and thus eliminatedfrom the scalogram G(f,t).

In block 308, at least one energy function L(t) is computed of thescalogram G(f,t). The at least one energy function can be of formL(t)=ΣG(f,t).

In some embodiments of the disclosure,f ranges from about 0 Hz to about1000 Hz. This frequency range will capture typical cardiac QRS activity.FIG. 5A depicts an energy function 502 of scalogam 404 in FIG. 4 wherefranges from about 0 Hz to about 1000 Hz.

In other embodiments of the disclosure,f covers a cardiac activityfrequency range between a preset lower frequency limit and a presetupper frequency limit. These embodiments are referred to herein as“bounded energy function” embodiments, and are advantageous in thecontext of capturing and decoupling near-field and far-field componentsof the electrogram signal S(t). One exemplary bounded energy functionhas a preset lower frequency limit of about 100 Hz and a preset upperfrequency limit of about 700 Hz; FIG. 5B depicts such a bounded energyfunction 504 of scalogram 404 in FIG. 4.

In still other embodiments of the disclosure, the at least one energyfunction L(t) is an energy frequency function. The energy frequencyfunction can be of form L^(Freq)(t)=f, where f is the highest frequency,between about 0 Hz and about 1000 Hz, at which the scalogram G(f,t)exceeds a preset (e.g., user-defined) peak threshold (e.g., about 90 Hzfor a near-field peak threshold and about 70 Hz for a far-field peakthreshold).

In block 310, at least one metric of the electrogram signal S(t) iscomputed in the wavelet domain using the energy function(s) L(t).Several metrics are described in further detail below.

Cardiac Activity Duration. A first metric that can be computed in thewavelet domain using the energy function(s) L(t) is the cardiac activityduration, denoted T^(QRS) throughout the Figures. To compute the cardiacactivity duration, the energy function L(t) can be converted to a pulsewave L^(Pulse)(t), where

${L^{Pulse}(t)} = \left\{ {\begin{matrix}{1,{{{if}\mspace{14mu}{L(t)}} > 0}} \\{0,{otherwise}}\end{matrix}.} \right.$can then be defined as the duration of the pulse wave L^(Pulse)(t).

Duration of Near- and Far-Field Components. A second metric that can becomputed in the wavelet domain using the energy fiinction(s) L(t), andin particular a bounded energy function L(t), is the duration ofmultiple near- and far-field components. An initial step in computingthis metric is to detect near-field and far-field activity peaks in theenergy function L(t).

Thus, for example, a plurality of local maximum peaks can be detected inthe energy function L(t), and each local maximum peak can be categorizedas a near-field peak, a far-field peak, or a noise peak, according tothe following logic, which is depicted in flowchart 800 in FIG. 8:

-   -   In block 802, a. local maximum peak can be identified;    -   In block 804, L(t) at the peak can be compared to a far field        threshold; if it does not exceed the far field threshold, then        the peak can be categorized as a noise peak and discarded (block        806);    -   In block 806, L(t) at the peak can be compared to a near field        threshold; if it exceeds the near field threshold, then the peak        can be categorized as a near field peak (block 810);    -   Otherwise, the peak can be categorized as a far field peak        (block 812).

In some embodiments, the preset far field threshold is about 2.5 timesthe length of the electrogram signal S(t) and the preset near fieldthreshold is about 5 times the length of the electrogram signal S(t).

Referring again to bounded energy function 504 in FIG. 5B, peak 508exceeds both the far field threshold 510 and the near field threshold512, and therefore would be categorized as a near-field peak.

Similar logic can be used to categorize peaks within the energyfrequency function L^(Freq)(t) described above as far field, near field,or noise, for example using the peak thresholds discussed above.

Once the peaks are categorized, it is possible to compute a near-fieldcomponent duration and/or a far-field component duration for theelectrogram signal S(t). The energy function (e.g., bounded energyfunction 502 in FIG. 5B) can be converted to a pulse wave L^(Pulse)(t)having one or more pulses, where

${L^{Pulse}(t)} = \left\{ {\begin{matrix}{1,{{{if}\mspace{14mu}{L(t)}} > 0}} \\{0,{otherwise}}\end{matrix}.} \right.$

The number of pulses in L^(Pulse)(t) is dependent upon the morphology ofthe electrogram signal S(t). If electrogram signal S(t) is from healthytissue with a single sharp QRS activity, such as signal 402 in FIG. 4,L^(Pulse)(t) will have a single pulse.

If the electrogram signal S(t) is of Form 1 described above, such assignal 602 in panel A of FIG. 6A, L^(Pulse)(t) will have multiplepulses, which result from the multiple peaks 604 (far-field peak), 606(near-field peak), 608 (far-field peak) shown in bounded energy function610 in FIG. 6B.

If the electrogram signal S(t) is of Form 2 described above, such assignal 702 in panel A of FIG. 7A, L^(Pulse)(t) will again have only asingle pulse, because the peaks 704 (near-field peak), 706 (near-fieldpeak), 708 (far-field peak), and 710 (noise peak) of bounded energyfunction 712 (see FIG. 7B) will be fused together.

If a pulse in L^(Pulse)(t) includes a near-field peak (e.g., peak 508 inFIG. 5B; peak 606 in FIG. 6B; peaks 704 and 706 in FIG. 7B), then thenear-field component duration for the electrogam signal S(t) can bedefined to be equal to the duration of the pulse. This is denotedthroughout the Figures as T^(MC1).

If, on the other hand, a pulse in L^(Pulse)(t) includes a fear-fieldpeak but not a near-field peak (e.g., peak 608 in FIG. 6B), then thefar-field component duration for the electrogram signal S(t) can bedefined to be equal to the duration of the pulse. This is denoted inFIGS. 6A and 6B as T^(MC2).

Number of Multiple Components. A third metric that can be computed inthe wavelet domain using the energy function(s) L(t) is the number ofcomponents present in the electrogram signal S(t). A plurality of localmaximum peaks can be detected in the energy function L(t), and eachlocal maximum peak can be categorized as a near-field peak, a far-fieldpeak, or a noise peak, as described above. The total number ofnear-field and far-field peaks in the energy function L(t) (that is, thetotal number of local maximum peaks excluding noise peaks) can bedefined to be the number of multiple components for the electrogramsignal S(t).

Slope of the Sharpest Component. A fourth metric that can be computed inthe wavelet domain using the energy function(s) L(t) is the slope of thesharpest component of the electrogram signal S(t). This can be computedby first identifying the maximum energy near-field peak of the pluralityof local maximum peaks in the energy function L(t). With reference toFIGS. 5B, 6B, and 7B, the maximum energy near-field peaks are peaks 508,606, and 704, respectively.

Once the maximum energy near-field peak has been identified, S(t) can beexamined within a preset refractory window about the peak to determinethe maximum slope thereof. More particularly, a maximum value of thefirst derivative S′(t) of the electrogram signal S(t) can be computedwithin the refractory window, and this maximum value of S′(t) can bedefined as the slope of the sharpest component of S(t). In embodimentsof the disclosure, the refractory window extends about 5 ms to eitherside of the peak (e.g., the refractory window is about 10 ms wide,centered on the peak).

Scalogram Width. The slope of the sharpest component of the electrogramsignal S(t) can also be inferred from the width of the scalogram G(f,t).The narrower the width of a peak in the scalogram G(f,t) correspondingto a peak in the electrogram signal S(t), the sharper the slope of theelectrogram signal S(t) about that peak.

For example, referring to peaks 609, 611 in electrogram signal 602 inpanel A of FIG. 6A, the width of the scalogram 603 in panel B has twocorresponding peaks 605, 607. Because the width of peak 605 is narrowerthan that of peak 607, the slope of electrogram signal 602 is sharperabout peak 609 than it is about peak 611. This relationship is alsovisible in FIG. 6B in the relative widths of the pulse durations T^(MC1)and T^(MC2) about peaks 606 and 608, respectively.

Energy Ratio. Yet another metric that can be computed in the waveletdomain using the energy function(s) L(t) is an energy ratio. Accordingto embodiments of the disclosure, two energy functions are computed: Ahigh frequency energy function L^(High)(t)=ΣG(f^(high),t) and a lowfrequency energy function L^(Low)(t)=ΣG(f^(Low),t). In aspects of thedisclosure, f^(Low) can be between about 10 Hz and about 60 Hz and 60 Hzand f^(High) can be between about 60 Hz and about 300 Hz, but otherranges (e.g., about 10 Hz to about 50 Hz for f^(Low) and about 50 Hz toabout 300 Hz for f^(High) or about 10 Hz to about 100 Hz for f^(Low) andabout 100 Hz to about 300 Hz for f^(High)) are contemplated.

Once the energy functions are computed, the ratio of L^(High)(t) toL^(Low)(t) can be computed.

Cycle Length-based Metrics. Still further metrics that can be computedin the wavelet domain using the energy function(s) L(t) are cyclelength-based metrics, such as mean cycle length and/or cycle lengthvariation/standard deviation. These cycle length-based metrics can alsobe computed using the ratio of L^(High)(t) to L^(Low)(t) describedabove. In particular, the ratio of L^(High)(t) to L^(Low)(t) can be usedto detect local activation times in the electrogram signal S(t) when theratio of L^(High)(t) to L^(Low)(t) exceeds a preset detection threshold.In embodiments, the preset detection threshold is about 0.5. Theordinarily skilled artisan will be familiar with the computation of meancycle lengths and cycle length variances/standard deviations from aplurality of local activation times.

Returning once more to flowchart 300 in FIG. 3, once the metric(s) havebeen computed, they can be displayed in block 312, for example as anelectrophysiology map on a cardiac model output on display 23 ofcomputer system 20 depicted in FIG. 1. As described above, varioustechniques familiar in the art can be used to generate a graphicalrepresentation from a plurality of electrophysiology data points,including the metrics described herein, such that a detailed discussionof the creation of an electrophysiology map is not necessary to anunderstanding of the present disclosure.

Although several embodiments of this invention have been described abovewith a certain degree of particularity, those skilled in the art couldmake numerous alterations to the disclosed embodiments without departingfrom the spirit or scope of this invention.

For example, the teachings herein can be applied not only tointracardiac electrogram signals, but also to surface ECG signals.

As another example, the teachings herein can be used to compute anelectrogram fractionation measure from sinus rhythm electrogram signals,paced electrogram signals, and arrhythmic electrogram signals (e.g.,atrial or ventricular tachycardia; atrial fibrillation).

All directional references (e.g., upper, lower, upward, downward, left,right, leftward, rightward, top, bottom, above, below, vertical,horizontal, clockwise, and counterclockwise) are only used foridentification purposes to aid the reader's understanding of the presentinvention, and do not create limitations, particularly as to theposition, orientation, or use of the invention, Joinder references(e.g., attached, coupled, connected, and the like) are to be construedbroadly and may include intermediate members between a connection ofelements and relative movement between elements. As such, joinderreferences do not necessarily infer that two elements are directlyconnected and in fixed relation to each other.

It is intended that all matter contained in the above description orshown in the accompanying drawings shall be interpreted as illustrativeonly and not limiting. Changes in detail or structure may be madewithout departing from the spirit of the invention as defined in theappended claims.

What is claimed is:
 1. A method of mapping cardiac activity, comprising:receiving an electrogram signal S(t) at an electroanatomical mappingsystem; and executing, via the electroanatomical mapping system, aprocess comprising: transforming the electrogram signal S(t) into thewavelet domain, thereby computing a scalogram G(f, t); computing atleast one energy function L(t) of the scalogram G(f, t), whereinL(t)=ΣG(f,t); and computing at least one metric of the electrogramsignal S(t) using the at least one energy function L(t) by computing apulse wave L^(Pulse)(t) having a pulse duration and one or more pulses,where ${L^{Pulse}(t)} = \left\{ {\begin{matrix}{1,{{{if}\mspace{14mu}{L(t)}} > 0}} \\{0,{otherwise}}\end{matrix};} \right.$ and displaying a graphical representation of theat least one metric of the electrogram signal S(t) on a graphicalrepresentation of a cardiac model.
 2. The method according to claim 1,wherein transforming the electrogram signal S(t) into the wavelet domaincomprises applying a continuous wavelet transformation to theelectrogram signal S(t) to compute the scalogram G(f, t).
 3. The methodaccording to claim 1, further comprising setting values of G(f, t) lessthan a preset noise threshold to zero.
 4. The method according to claim1, wherein the at least one metric of the electrogram signal S(t)comprises a QRS activity duration for the electrogram signal S(t),wherein the QRS activity duration for the electrogram signal S(t) isdefined to be equal to the pulse duration of the pulse waveL^(Pulse)(t).
 5. The method according to claim 1, wherein f is within acardiac activity frequency range defined by a preset lower frequencylimit and a preset upper frequency limit.
 6. The method according toclaim 5, further comprising: detecting a plurality of local maximumpeaks in the at least one energy function L(t); and categorizing eachlocal maximum peak of the plurality of local maximum peaks as anear-field peak, a far-field peak, or a noise peak.
 7. The methodaccording to claim 6, wherein categorizing each local maximum peak ofthe plurality of local maximum peaks as a near-field peak, a far-fieldpeak, or a noise peak comprises: categorizing a local maximum peak as anear-field peak if the at least one energy function L(t) exceeds apreset near field threshold at the local maximum peak; categorizing thelocal maximum peak as a far-field peak if the at least one energyfunction L(t) exceeds a preset far field threshold and not the presetnear field threshold at the local maximum peak; and categorizing thelocal maximum peak as a noise peak otherwise.
 8. The method according toclaim 6, wherein the at least one metric of the electrogram signal S(t)comprises at least one of a near-field component duration and afar-field component duration for the electrogram signal S(t), computedby a method comprising: for each pulse of the one or more pulses of thepulse wave L^(Pulse)(t): if the pulse includes a near-field peak,defining the near-field component duration for the electrogram signalS(t) to be equal to a duration of the pulse; and if the pulse includes afar-field peak, defining the far-field component duration for theelectrogram signal S(t) to be equal to the duration of the pulse.
 9. Themethod according to claim 6, wherein the at least one metric of theelectrogram signal S(t) comprises a number of multiple components forthe electrogram signal S(t), wherein the number of multiple componentsfor the electrogram signal S(t) is defined to be equal to a total numberof the plurality of local maximum peaks in the at least one energyfunction L(t).
 10. The method according to claim 6, wherein the at leastone metric of the electrogram signal S(t) comprises a slope of asharpest component of the electrogram signal S(t).
 11. The methodaccording to claim 10, wherein the slope of a sharpest component of theelectrogram signal S(t) is computed by a method comprising: identifyinga maximum energy near-field peak of the plurality of local maximum peaksin the at least one energy function L(t); computing a maximum value of afirst derivative S′(t) of the electrogram signal S(t) within a presetrefractory window surrounding the maximum energy near-field peak; anddefining the maximum value of the first derivative S′(t) of theelectrogram signal S(t) within the preset refractory window as the slopeof the sharpest component of the electrogram signal S(t).
 12. The methodaccording to claim 1, wherein: the at least one energy function L(t)comprises: computing a high frequency energy functionL^(High)(t)=ΣG(f^(High),t); and computing a low frequency energyfunction L^(Low)(t)ΣG (f^(Low),t); and computing the at least one metricof the electrogram signal S(t) comprises computing a ratio ofL^(High)(t) to L^(Low)(t).
 13. The method according to claim 12, whereinf^(High) is between 60 Hz and 300 Hz and f^(Low) is between 10 Hz and 60Hz.
 14. The method according to claim 12, wherein the at least onemetric of the electrogram signal S(t) comprises a cycle-length basedmetric computed using the ratio of L^(High)(t) to L^(Low)(t).
 15. Themethod according to claim 14, wherein the cycle-length based metric iscomputed using the ratio of L^(High)(t) to L^(Low)(t) based upon aplurality of local activation times detected when the ratio ofL^(High)(t) to L^(Low)(t) exceeds a preset threshold.
 16. A system formapping cardiac activity, comprising: a wavelet transformation processorconfigured: to receive an electrogram S(t); to transform the electrogramsignal S(t) into the wavelet domain, thereby computing a scalogram G(f,t); and to compute at least one energy function L(t) of the scalogramG(f, t), wherein L(t)=ΣG(f,t); and a mapping processor configured to:compute at least one metric of the electrogram signal S(t) in thewavelet domain using the at least one energy function L(t), comprisingcomputing a pulse wave L^(Pulse)(t) having a pulse duration and one ormore pulses, where ${L^{Pulse}(t)} = \left\{ {\begin{matrix}{1,{{{if}\mspace{14mu}{L(t)}} > 0}} \\{0,{otherwise}}\end{matrix};} \right.$  and output a graphical representation of the atleast one metric of the electrogram signal S(t) on a graphicalrepresentation of a cardiac model; and a display to display thegraphical representation.
 17. The system according to claim 16, whereinthe at least one metric of the electrogram signal S(t) is selected fromthe group consisting of: a QRS activity duration for the electrogramsignal S(t); a near-field component duration for the electrogram signalS(t); a far-field component duration for the electrogram signal S(t); anumber of multiple components for the electrogram signal S(t); a slopeof a sharpest component of the electrogram signal S(t); a scalogramwidth; an energy ratio in the electrogram signal S(t); and acycle-length based metric of the electrogram signal S(t).